Directly Identify Unexpected Instances in the Test Set by Entropy Maximization

Authors:
Chaofeng Sha;Zhen Xu;Xiaoling Wang;Aoying Zhou
Affiliations:
Department of Computer Science and Engineering, Fudan University, Shanghai, China 200433;Department of Computer Science and Engineering, Fudan University, Shanghai, China 200433;Shanghai Key Laboratory of Trustworthy Computing Institute of Massive Computing, East China Normal University, Shanghai, China 200062;Shanghai Key Laboratory of Trustworthy Computing Institute of Massive Computing, East China Normal University, Shanghai, China 200062
Venue:
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Year:
2009

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Abstract

In real applications, a few unexpected examples unavoidably exist in the process of classification, not belonging to any known class. How to classify these unexpected ones is attracting more and more attention. However, traditional classification techniques can't classify correctly unexpected instances, because the trained classifier has no knowledge about these. In this paper, we propose a novel entropy-based method to the problem. Finally, the experiments show that the proposed method outperforms previous work in the literature.